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Accurate modeling of electric power transmission networks (EPTNs) is essential for real-time monitoring, operational awareness, and contingency analysis in power systems. Representing EPTNs as graphs with nodes and edges offers a powerful abstraction of the network topology. However, inferring this topology using only phasor measurement unit (PMU) data remains a challenge, especially with no prior network information. In this study, a quantum-classical hybrid approach based on the quantum approximate optimization algorithm (QAOA) to infer a transmission network graph model (TNGM) directly from PMU data is presented. The proposed approach utilizes a cost function incorporating the difference between power mismatch and mean power loss to guide one-to-one branch matching. Furthermore, the effect of quantum circuit depth is investigated to achieve 100% accuracy in TNGM construction. Typical results are presented on the two-area four-machine power system.
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Data-Driven Graph Construction of Power Flow Graphs for Electric Power Transmission Networks
A comprehensive understanding of the topology of the electric power transmission network (EPTN) is essential for reliable and robust control of power systems. While existing research primarily relies on domain-specific methods, it lacks data-driven approaches that have proven effective in modeling the topology of complex systems. To address this gap, this paper explores the potential of data-driven methods for more accurate and adaptive solutions to uncover the true underlying topology of EPTNs. First, this paper examines Gaussian Graphical Models (GGM) to create an EPTN network graph (i.e., undirected simple graph). Second, to further refine and validate this estimated network graph, a physics-based, domain specific refinement algorithm is proposed to prune false edges and construct the corresponding electric power flow network graph (i.e., directed multi-graph). The proposed method is tested using a synchrophasor dataset collected from a two-area, four-machine power system simulated on the real-time digital simulator (RTDS) platform. Experimental results show both the network and flow graphs can be reconstructed using various operating conditions and topologies with limited failure cases.
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- PAR ID:
- 10593130
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-7488-9
- Page Range / eLocation ID:
- 346 to 353
- Subject(s) / Keyword(s):
- critical infrastructure, data-driven modeling, electric power transmission, Gaussian graphical models, graph construction
- Format(s):
- Medium: X
- Location:
- Miami, FL, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICMLA61862.2024.00053
